Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas
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Abstract (arXiv)
The success of Large Language Models (LLMs) in multicultural environments hinges on their ability to understand users' diverse cultural backgrounds. We measure this capability by having an LLM simulate human profiles representing various nationalities within the scope of a questionnaire-style psychological experiment. Specifically, we employ GPT-3.5 to reproduce reactions to persuasive news articles of 7,286 participants from 15 countries; comparing the results with a dataset of real participants sharing the same demographic traits. Our analysis shows that specifying a person's country of residence improves GPT-3.5's alignment with their responses. In contrast, using native language prompting introduces shifts that significantly reduce overall alignment, with some languages particularly impairing performance. These findings suggest that while direct nationality information enhances the model's cultural adaptability, native language cues do not reliably improve simulation fidelity and can detract from the model's effectiveness.
Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). Builds on 40 of its 68 references that are in the map. First posted on arXiv 13 Aug 2024.
Builds on
40 of 68 references.
References · 40
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Citing papers per year: 2024: 1; 2025: 23; 2026: 13.
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- 2025The American Ghost in the Machine: How language models align culturally and the effects of cultural prompting13 Dec 2025
- 2025DeepSeek's WEIRD Behavior: The cultural alignment of Large Language Models and the effects of prompt language and cultural prompting10 Dec 2025
- 2025Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests25 Oct 2025
- 2025Counterfactual Reasoning for Steerable Pluralistic Value Alignment of Large Language Models21 Oct 2025
- 2025Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model's Empathy11 Oct 2025
- 2025Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning25 Aug 2025
- 2025Large Language Models Do Not Simulate Human Psychology9 Aug 2025
- 2025The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models21 Jul 2025
- 2025Improving the Distributional Alignment of LLMs using Supervision1 Jul 2025
- 2025Free Lunch for User Experience: Crowdsourcing Agents for Scalable User Studies29 May 2025
- 2025Multilingual Prompting for Improving LLM Generation Diversity21 May 2025
- 2025Images Amplify Misinformation Sharing in Vision-Language Models19 May 2025
- 2025AdAEM: An Adaptively and Automated Extensible Measurement of LLMs' Value Difference18 May 2025
- 2025LoRe: Personalizing LLMs via Low-Rank Reward Modeling20 Apr 2025
- 2025SOTOPIA-S4: a user-friendly system for flexible, customizable, and large-scale social simulation19 Apr 2025
- 2025Visual moral inference and communication12 Apr 2025
- 2025CAReDiO: Cultural Alignment via Representativeness and Distinctiveness Guided Data Optimization9 Apr 2025
- 2025SaudiCulture: A Benchmark for Evaluating Large Language Models Cultural Competence within Saudi Arabia21 Mar 2025
- 2025Assessing Agentic Large Language Models in Multilingual National Bias25 Feb 2025
- 2025Exploring Persona Sentiment Sensitivity in Personalized Dialogue Generation17 Feb 2025
- 2025Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations10 Feb 2025
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- 2025DeepSeek's WEIRD Behavior: The cultural alignment of Large Language Models and the effects of prompt language and cultural prompting10 Dec 2025
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- 2025Images Amplify Misinformation Sharing in Vision-Language Models19 May 2025
- 2025Specializing Large Language Models to Simulate Survey Response Distributions for Global Populationscited by 53
- 2025The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Modelscited by 42
- 2024LLM-GLOBE: A Benchmark Evaluating the Cultural Values Embedded in LLM Outputcited by 27
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- 2025Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model's Empathycited by 10
- 2025SaudiCulture: A Benchmark for Evaluating Large Language Models Cultural Competence within Saudi Arabiacited by 8
- 2025CAReDiO: Cultural Alignment via Representativeness and Distinctiveness Guided Data Optimizationcited by 6
- 2025Made-in China, Thinking in America:U.S. Values Persist in Chinese LLMscited by 5
- 2025AdAEM: An Adaptively and Automated Extensible Measurement of LLMs' Value Differencecited by 4
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12 most cited of 27 All 27 (JSON)
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- 2026Reach Into The CHOIR: Free-List Elicitation Uncovers Distinct Model Voices in LLM Ensembles29 Sep 2026
- 2026DiSCo: A Distribution-First Steering and Cultural Prior Evaluation Framework for Measuring Cultural Preference Bias in LLMs9 Sep 2026
- 2026AI YOU Town: Make Friends and Money with Your Digital Twin12 Jul 2026
- 2026ExpertIVS: Sociological Expert Driven Individual Value Simulation in Large Language Models17 Jun 2026
- 2026Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities11 Jun 2026
- 2026Beyond Averages: Evaluating LLMs on Human Survey Replication at the Distributional Level8 Jun 2026
- 2026Occupational Prompting Reveals Cultural Bias in Large Language Models19 May 2026
- 2026Lessons Without Borders? Evaluating Cultural Alignment of LLMs Using Multilingual Story Moral Generation9 Apr 2026
- 2026Stories of Your Life as Others: A Round-Trip Evaluation of LLM-Generated Life Stories Conditioned on Rich Psychometric Profiles7 Apr 2026
- 2026From National Curricula to Cultural Awareness: Constructing Open-Ended Culture-Specific Question Answering Dataset8 Jan 2026
- 2025Made-in China, Thinking in America:U.S. Values Persist in Chinese LLMs13 Dec 2025
12 newest of 27 All 27 (JSON)